Report Number: CS-TR-94-1515
Institution: Stanford University, Department of Computer Science
Title: Retrieving Semantically Distant Analogies
Author: Wolverton, Michael
Date: June 1994
Abstract: Techniques that have traditionally been useful for retrieving
same-domain analogies from small single-use knowledge bases,
such as spreading activation and indexing on selected
features, are inadequate for retrieving cross-domain
analogies from large multi-use knowledge bases. Blind or
near-blind search techniques like spreading activation will
be overwhelmed by combinatorial explosion as the search goes
deeper into the KB. And indexing a large multi-use KB on
salient features is impractical, largely because a feature
that may be useful for retrieval in one task may be useless
for another task. This thesis describes Knowledge-Directed
Spreading Activation (KDSA), a method for retrieving
analogies in a large semantic network. KDSA uses
task-specific knowledge to guide a spreading activation
search to a case or concept in memory that meets a desired
similarity condition. The thesis also describes a specific
instantiation of this method for the task of innovative
design.
KDSA has been validated in two ways. First, a theoretical
model of knowledge base search demonstrates that KDSA is
tractable for retrieving semantically distant analogies under
a wide range of knowledge base configurations. Second, an
implemented system that uses KDSA to find analogies for
innovative design shows that the method is able to retrieve
semantically distant analogies for a real task. Experiments
with that system show trends as the knowledge base size grows
that suggest the theoretical model's prediction of large
knowledge base tractability is accurate.
http://i.stanford.edu/pub/cstr/reports/cs/tr/94/1515/CS-TR-94-1515.pdf